Large language models have become increasingly capable of handling a wide variety of complex tasks, yet their ability to manage extended context and generalize across multiple domains of world knowledge remains limited. The novel approach outlined here significantly enhances a model's capacity to process long-form texts and integrate factual knowledge through targeted modifications to its attention mechanisms and training strategies. By incorporating sparse attention frameworks and memoryaugmented networks, the architecture is able to efficiently handle extended inputs without sacrificing performance. World knowledge augmentation, achieved through tasks such as openbook question answering and fact-checking, further improves the model's ability to generalize across diverse domains, ensuring factual accuracy and consistency in its outputs. Experimental evaluations demonstrate that the modified model not only outperforms baseline models in long-context comprehension but also achieves superior performance in cross-domain generalization tasks. The findings highlight the potential for architectural improvements to enhance both scalability and accuracy in handling complex tasks that require deep contextual understanding and the integration of structured knowledge.